From the course: Machine Learning in Telecommunication: From Basics to Real-World Cases
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ML workflow
From the course: Machine Learning in Telecommunication: From Basics to Real-World Cases
ML workflow
(light music) - [Instructor] In this session, we'll explore machine learning frameworks and tools. Our focus will be to understand not just the critical flow of machine learning, but also the practical tools that help in building real world machine learning systems. After understanding this machine learning flow, we'll walk through a project simulation and live application to see how everything fits together. Let's start by understanding the machine learning pipeline. Think of it as a roadmap. The first step is the data collection. It's about gathering relevant, high-quality data as machine learning is a data driven. Next to that is a data preparation, which involves cleaning, formatting, and sometimes augmenting data, so that it can be fed into models effectively. Then, it's choosing a model. It means selecting the right algorithm based on data characteristics and project objectives. Next to that is training the model. It is the phase where the algorithm learns from data. And then is…
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